Abstract: DeepTensor is a computationally efficient framework for low-rank decomposition of matrices and tensors using deep generative networks. We decompose a tensor as the product of low-rank tensor ...

When the FORTRAN programming language debuted in 1957, it transformed how scientists and engineers programmed computers. Complex calculations could suddenly be expressed in concise, math-like notation ...

TPUs are Google’s specialized ASICs built exclusively for accelerating tensor-heavy matrix multiplication used in deep learning models. TPUs use vast parallelism and matrix multiply units (MXUs) to ...

They managed to cut the size of the AI reasoning model by more than half—and claim it can now answer politically sensitive questions once off limits in Chinese AI systems. A group of quantum ...

Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...

The tech industry is on a tear, building data centers for AI as quickly as they can buy up the land. The sky-high energy costs and logistical headaches of managing all those data centers have prompted ...

Researchers from The University of New Mexico and Los Alamos National Laboratory have developed a novel computational framework that addresses a longstanding challenge in statistical physics. The ...

(A) Illustration of a convolutional neural network (NN) whose variational parameters (T) are encoded in the automatically differentiable tensor network (ADTN) shown in (B). The ADTN contains many ...

This review represents a strategic blueprint shaped by the world’s leading quantum experts. We believe that solving the most complex challenges requires collective intelligence and collaboration.” — ...

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